Background and ObjectivesDue to the inevitability of waiting time for surgery, this problem seems to have become more pronounced since the outbreak of COVID-19, and due to the high incidence of preoperative hydronephrosis in upper urinary tract urothelial carcinoma (UTUC) patients, it is particularly important to explore the impact of preoperative waiting time and hydronephrosis on upper urinary urothelial carcinoma.Methods316 patients with UTUC who underwent radical surgery at a high-volume center in China between January 2008 and December 2019 were included in this study. We retrospectively collected the clinicopathologic data from the medical records, including age, sex, smoking history, ECOG performance status (ECOG PS), body mass index (BMI), tumor location and size, number of lesions, T stage, N stage, surgical approach and occurrence of hydronephrosis, lymph node invasion, lymph node dissection, surgical margin, tumor necrosis, infiltrative tumor architecture, lymphovascular invasion and concomitant bladder cancer. Surgical wait time was defined as the interval between initial imaging diagnosis and radical surgery of UTUC. Hydronephrosis was defined as abnormal dilation of the renal pelvis and calyces due to obstruction of the urinary system. Firstly, all patients were divided into short-wait (<31 days), intermediate-wait (31-90 days) and long-wait (>90 days) groups according to the surgical wait time. The clinicopathological characteristics of each group were evaluated and the survival was compared. For patients with hydronephrosis, we subsequently divided them into two groups: short-wait (≤60 days) and long-wait (>60 days) groups according to the surgical wait time. Univariate and multivariate COX regression analysis were performed to evaluate the prognostic risk factor for patients with hydronephrosis.ResultsA total of 316 patients with UTUC were included in this study with a median surgical wait time of 22 days (IQR 11-71 days). Of the 316 patients, 173 were classified into the short-wait group (54.7%), 69 into the intermediate-wait group (21.8%) and 74 into the long-wait group (23.5%). The median follow-up time for all patients was 43 months (IQR 28-67months). The median surgical wait times of the short-wait, intermediate-wait and long-wait group were12 days (IQR 8-17days), 42days (IQR 37-65days) and 191days (IQR 129-372days), respectively. The 5-year overall survival (OS) of all patients was 54.3%. The 5-year OS of short-wait, intermediate-wait and long-wait groups were 56.4%, 59.3% and 35.1%, respectively (P=0.045). The 5-year cancer-specific survival (CSS) of short-wait, intermediate-wait and long-wait groups were 65.8%, 70.9% and 39.6%, respectively (P=0.032). In the subgroup analysis, we divided 158 UTUC patients with hydronephrosis into short-wait group (≤60 days) and long-wait group (> 60 days), 120 patients were included in the short-wait group and 38 patients in the long-wait group. The median surgical wait times of the short-wait and long-wait group were 14days (IQR 8-28days) and 174days (IQR 100-369days), respectively. The 5-year OS of long-wait group was significantly lower than the OS of short-wait group (44.2% vs. 55.1%, P =0.023). The 5-year CSS of long-wait and short-wait group were 49.1% and 61.7%, respectively (P=0.041). In multivariate Cox regression analysis of UTUC patients with hydronephrosis, surgical wait time, tumor grade, pathological T stage, and tumor size were independent risk factors for OS and CSS. Lymph node involvement was also a prognostic factor for CSS.ConclusionFor patients with UTUC, the surgical wait time should be limited to less than 3 months. For UTUC patients with hydronephrosis, the OS and CSS of patients with surgical wait time of more than 60 days were relatively shorted than those of patients with surgical wait time of less than 60 days.

Background: The number of asymptomatic infected patients with coronavirus disease 2019 (COVID-2019) is rampaging around the world but limited information aimed on risk factors of asymptomatic infections. The purpose of this study is to investigate the risk factors of symptoms onset and clinical features in asymptomatic COVID-19 infected patients. Methods: A retrospective study was performed in 70 asymptomatic COVID-2019 infected patients confirmed by nucleic acid tests in Hunan province, China between 28 January 2020 and 18 February, 2020. The epidemiological, clinical features and laboratory data were reviewed and analyzed. Presence or absence at the onset of symptoms was taken as the outcome. A Cox regression model was performed to evaluate the potential predictors of the onset of symptoms. Results: The study included 36 males and 34 females with a mean age of 33.24±20.40 years (range, 0.5-84 years). There were 22 asymptomatic carriers developed symptoms during hospitalization isolated observation, and diagnosed as confirmed cases, while 48 cases remained asymptomatic throughout the course of disease. Of 70 asymptomatic patients, 14 (14/70, 20%) had underlying diseases, 3 (3/70, 4.3%) had drinking history, and 11 (11/70, 15.7%) had smoking history. 22 patients developed symptoms onset of fever (4/22, 18.2%), cough (13/22, 59.1%), chest discomfort (2/22, 9.1%), fatigue (1/22, 4.5%), pharyngalgia (1/22, 4.5%) during hospitalization; only one (1/22, 4.5%) patient developed signs of both cough and pharyngalgia. Abnormalities on chest CT were detected among 35 of the 69 patients (50.7%) after admission, except for one pregnant woman had not been examined. 4 (4/70, 5.7%) and 8 (8/70, 11.4%) cases showed leucopenia and lymphopenia. With the effective antiviral treatment, all the 70 asymptomatic infections had been discharged, none cases developed severe pneumonia, admission to intensive care unit, or died. The mean time from nucleic acid positive to negative was 13.2±6.84 days. Cox regression analysis showed that smoking history (P=0.028, hazard ratio=4.49, 95% CI 1.18-17.08) and existence of pulmonary disease (P=0.038, hazard ratio=7.09, 95% CI 1.12-44.90) were risk factors of the onset of symptoms in asymptomatic carries. Conclusion: The initially asymptomatic patients can develop mild symptoms and have a good prognosis. History of smoking and pulmonary disease was prone to illness onset in asymptomatic patients, and it is necessary to be highly vigilant to those patients.

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Purpose:The purpose of the study was to evaluate the diagnostic significance of two new and a few clinical markers for prostate cancer (PCa) at various prostate volumes (PV). Methods:The study subjects were divided into two groups. Among them, there were 70 cases in the PV ≤30 ml group (benign prostatic hyperplasia [BPH]: 32 cases, PCa: 38 cases) and 372 cases in the PV > 30 ml group (BPH: 277 cases, PCa: 95 cases). SPSS 26.0 and GraphPad Prism 8.0 were used to construct their receiver operating characteristic (ROC) curves for diagnosing PCa and calculating their area under the ROC curve (AUC). Results:In the PV ≤30 ml group, the diagnostic parameters based on prostate-specific antigen (PSA) had a decreased diagnostic significance for PCa. In the PV > 30 ml group, PSAD (AUC = 0.709), AVR (AVR = Age/PV, AUC = 0.742), and A-PSAD (A-PSAD = Age×PSA/PV, AUC = 0.736) exhibited moderate diagnostic significance for PCa, which was better than PSA-AV (AUC = 0.672), free PSA (FPSA, AUC = 0.509), total PSA (TPSA, AUC = 0.563), (F/T) PSA (AUC = 0.540), and (F/T)/PSAD (AUC = 0.663).

Objective. Improving health literacy in infectious diseases is a direct manifestation of the solid advance in disease control and prevention. Our study is aimed at exploring applying synthetic minority oversampling technique (SMOTE) in the prediction assessment of whether residents and business employees have infectious disease health literacy. Methods. The Chinese resident infectious disease health literacy evaluation scale was used to investigate the associated variables. The screened variables were input variables and the presence or absence of infectious diseases health literacy as outcome variables. Logistic regression, random forest, and support vector machine (SVM) models were built in the data sets before and after treatment by the SMOTE algorithm, respectively, and the performance of the models was evaluated by receiver operating characteristic curves (ROC). Results. Logistic regression, random forest, and SVM achieved accuracies of 0.828, 0.612, and 0.654 before SMOTE algorithm processing, and the areas under the ROC curves (AUCs) of the three models were 0.754, 0.817, and 0.759, respectively. The accuracies were 0.938, 0.911, and 0.894 after SMOTE algorithm processing, and the AUCs of the three models were 0.913, 0.925, and 0.910, respectively. Conclusions. The random forest model based on the SMOTE has high application value in assessing whether residents versus enterprise employees have infectious disease health literacy.